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Converging evidence suggest that motor training is associated with early and late changes of the cortical motor system. Transcranial magnetic stimulation (TMS) offers the possibility to study plastic rearrangements of the motor system in physiological and pathological conditions. We used TMS to characterize long-term changes in upper limb motor cortical representation and interhemispheric inhibition associated with bimanual skill training in pianists who started playing in an early age. Ipsilateral silent period (iSP) and cortical TMS mapping of hand muscles were obtained from 30 strictly right-handed subjects (16 pianists, 14 naïve controls), together with electromyographic recording of mirror movements (MMs) to voluntary hand movements. In controls, motor cortical representation of hand muscles was larger on the dominant (DH) than on the non-dominant hemisphere (NDH). On the contrary, pianists showed symmetric cortical output maps, being their DH less represented than in controls. In naïve subjects, the iSP was smaller on the right vs left abductor pollicis brevis (APB) indicating a weaker inhibition from the NDH to the DH. In pianists, interhemispheric inhibition was more symmetric as their DH was better inhibited than in controls. Electromyographic MMs were observed only in naïve subjects (7/14) and only to voluntary movement of the non-dominant hand. Subjects with MM had a lower iSP area on the right APB compared with all the others. Our findings suggest a more symmetrical motor cortex organization in pianists, both in terms of muscle cortical representation and interhemispheric inhibition. Although we cannot disentangle training-related from preexisting conditions, it is possible that long-term bimanual practice may reshape motor cortical representation and rebalance interhemispheric interactions, which in naïve right-handed subjects would both tend to favour the dominant hemisphere.

With the performance of central processing units (CPUs) having effectively reached a limit, parallel processing offers an alternative for applications with high computational demands. Modern graphics processing units (GPUs) are massively parallel processors that can execute simultaneously thousands of light-weight processes. In this study, we propose and implement a parallel GPU-based design of a popular method that is used for the analysis of brain magnetic resonance imaging (MRI). More specifically, we are concerned with a model-based approach for extracting tissue structural information from diffusion-weighted (DW) MRI data. DW-MRI offers, through tractography approaches, the only way to study brain structural connectivity, non-invasively and in-vivo. We parallelise the Bayesian inference framework for the ball & stick model, as it is implemented in the tractography toolbox of the popular FSL software package (University of Oxford). For our implementation, we utilise the Compute Unified Device Architecture (CUDA) programming model. We show that the parameter estimation, performed through Markov Chain Monte Carlo (MCMC), is accelerated by at least two orders of magnitude, when comparing a single GPU with the respective sequential single-core CPU version. We also illustrate similar speed-up factors (up to 120x) when comparing a multi-GPU with a multi-CPU implementation. Ingeniería, Industria y Construcción